IoLT - Internet of Living Things

Project description

The project aims to create a Network of Excellence by integrating IoT technological research with applied research on specific, biological IoT applications. It is creating an opensource IoLT programming platform based on JavaScript. It can execute applications on cheap, low capacity IoT devices by providing easy to use programming interfaces, thus enabling application development for researchers of any discipline. Technological developments is addressing the JavaScript executor engine (utilizing the industry-accepted JerryScript project), software-hardware porting, programming environment, secure management algorithms and software quality. The IoLT application areas include the development of a smart pot for plants enabling complex plant phenotyping using medium-high throughput characterization of plant growth and physiological status; the development of a smart watch for performing actigraphy to investigate ultradian activity levels of patients in psychosocial treatments; and the development of Lab-on-a-chip systems for enhanced microfluidic diagnostic technologies for high-throughput cell analysis. The partners are planning to engage in R&D in international cooperation in the following areas: JavaScript execution engine, IoT development environment, static analysis, source code quality and vulnerability checks, device drivers, communications, IoT cloud infrastructure, algorithms for data safety and security.

Duration: 2017.02.01-2021.10.29.

Partners: University of Szeged (SZTE),
Biological Research Center (SZBK)

Scientific coordinator: Prof. Dr. Tibor Gyimothy

Work packages, leaders and research tasks

F1. IoLT Programming platform

Working group leaders:

• Gabor Loki, SZTE
• Zoltan Gingl, SZTE

F2. Secure control and software quality

Working group leaders:

• Istvan Siket, SZTE
• Gabor Loki, SZTE
• Mark Jelasity, SZTE
• Rudolf Ferenc, SZTE
• Attila Kertesz, SZTE

F3. Smart IoLT Applications

Working group leaders:

• Imre Vass, SZBK
• Andras Der, SZBK
• Peter Galajda, SZBK
• Peter Horvath, SZBK
• Istvan Szendi, SZTE

Selected publications

F1. IoLT Programming platform

• Z. Herczeg and G. Loki. Evaluation and Comparison of Dynamic Call Graph Generators for JavaScript. In Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE. DOI: 10.5220/0007752904720479 (2019)
• R. Mingesz and D. Farago. Implementing software defined noise generators. In the 25th International Conference on Noise and Fluctuations. DOI: 10.5075/epfl-ICLAB-ICNF-269296 (2019)
• G. Loki and P. Gal. JavaScript Guidelines for JavaScript Programmers - A Comprehensive Guide for Performance Critical JS Programs. In Proceedings of the 13th International Conference on Software Technologies - Volume 1: ICSOFT. DOI: 10.5220/0006918904310438 (2018)

F2. Secure control and software quality

• A. Kertesz, T. Pflanzner and T. Gyimothy. A Mobile IoT Device Simulator for IoT-Fog-Cloud Systems. In Journal of Grid Computing. DOI: 10.1007/s10723-018-9468-9 (2019)
• E. Tremel, K. Birman, R. Kleinberg and Márk Jelasity. Anonymous, fault-tolerant distributed queries for smart devices. In ACM Transactions on Cyber-Physical Systems. DOI: 10.1145/3204411 (2018)
• P. Hegedus, I. Kadar, R. Ferenc and T. Gyimothy. Empirical Evaluation of Software Maintainability Based on a Manually Validated Refactoring Dataset. In Information and Software Technology. DOI: 10.1016/j.infsof.2017.11.012 (2017)

F3. Smart IoLT Applications

• G. Vadai, A. Antal and Z. Gingl. Spectral analysis of fluctuations in humans’ daily motion using location data. In Fluctuation and Noise Letters. DOI: 10.1142/S0219477519400108 (2019)
• K. Paul, J. Pauk, A. Kondic-Spika, H. Grausgruber, T. Allahverdiyev, L. Sass and I. Vass. Co-occurrence of Mild Salinity and Drought Synergistically Enhances Biomass and Grain Retardation in Wheat. In Frontiers in Plant Science. DOI: 10.3389/fpls.2019.00501 (2019)
• F. Piccinini, T. Balassa, A. Szkalisity, Cs. Molnar, L. Paavolainen, K. Kujala, K. Buzas, M. Sarazova, V. Pietiainen, U. Kutay, K. Smith and P. Horvath. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data. In Cell Systems. DOI: 10.1016/j.cels.2017.05.012 (2017)